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106 articles for “EES”
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Reduction of Ocular Artifacts in Single Channel EEG by EEMD-IMF Thresholding
Abstract: AbstractElectroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to the movements of eye, heart, muscles and power line interference. Among these ocular activities creates significant artifacts and makes the analysis difficult. In this paper, ensemble empirical mode decomposition (EEMD) inspired by wavelet thresholding is used for the correction of ocular artifacts (OA) in EEG signals. Unlike the conventional …
Published in Recent Trends in Electronics Communication Systems · Vol. 5, Issue 1, 2018 · pp. 17–25 Read article
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A Survey on EEG Signals and Applications of EEG in Different Fields
Abstract: AbstractEEG being in trend for a last few decades is now being used in several different fields for different technological benefits. Brain Computer Interface is one of the useful applications of EEG Signals which gave rise to several other subfields like Marketing, Gaming etc. EEG signals are popularly used in medical fields as detection of neurological diseases has become simpler and cheaper because of multiple researches in EEG signals as …
Published in Current Trends in Signal Processing · Vol. 10, Issue 1, 2020 · pp. 15–18 Read article
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High-Definition Electroencephalography: A New Horizon in Neurological Pathology Research
Abstract: The advent of high-density electroencephalography (HD-EEG) has catalyzed a paradigm shift in the exploration of neurological pathologies. This editorial underscore its transformative potential in elucidating brain dynamics and refining diagnostic approaches for a spectrum of conditions, spanning from epilepsy and dementia to cognitive impairments in preterm infants. Our objective is to optimize the utility of HD-EEG by emphasizing the imperative for methodological homogenization and fostering collaborative endeavors. The remarkable spatial …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 15–21 Read article
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Investigations On Use of Poly(3,4-Ethylenedioxythiophene): Poly (Styrene Sulfonic Acid) (PEDOT: PSS) Conductive Polymers for Design of Improved EEG Based Brain Computer Interface for Seizure Control and Analysis
Abstract: This research explores the application of Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) conductive polymers in the design of an enhanced Electroencephalography (EEG)-based Brain-Computer Interface (BCI) for seizure control and analysis. PEDOT: PSS, known for its high conductivity, flexibility, and biocompatibility, is employed to improve the efficiency and sensitivity of EEG electrodes, addressing challenges such as signal noise, skin-electrode impedance, and user comfort. The study evaluates the material’s properties, including its electrical conductivity, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 223–241 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation Read article
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Advanced Polymer Nanocomposite EEG Electrodes for Enhanced Epileptic Seizure Detection: A Comparative Analysis
Abstract: Electroencephalography (EEG) has been very important in the detection of epileptic seizures so as to enable successful diagnosis, surveillance and therapy of epilepsy. Nevertheless, EEG electrodes based on traditional metals may be limited due to high or high contact impedance, lack of biocompatibility, discomfort to patients and prone to motion artifacts, which interfere with signal quality and diagnostic adequacy. The recent progress in material science has resulted in coming up …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 Read article
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Optimization of FLOTOX in EEPROM Design
Abstract: Abstract: Electrically Erasable Programmable Read-Only Memory (EEPROM) is a non-volatile memory (NVM) which is used in computers, integrated into microcontrollers for smart cards and remote keyless system, and other electronic devices to store relatively small amounts of data but allowing individual bytes to be erased and reprogrammed. Here the sizes of the floating gate & control gate have been altered to find the impacts for programming events and determine the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 6, Issue 1, 2019 · pp. 1–9 Read article
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Review of Machine Technique for Prediction of Depression Using EEG Signal
Abstract: Depression is a prevalent mental health disorder that affects millions worldwide, often leading to significant personal and societal burdens. Traditional diagnostic methods for depression rely heavily on subjective assessments, which can be prone to bias and inconsistency. In recent years, there has been a growing interest in utilizing machine learning techniques to predict depression based onelectroencephalogram (EEG) signals, offering a promising avenue for more objective and reliable diagnosis. This review …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 3, 2023 · pp. 23–29 Read article
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Music on Mind and Body: A Simultaneous EEG and EMG Study to Quantify Emotions from Hindustani Classical Music
Abstract: With the advent of various techniques to assess the bioelectric signals on the surface of the body, it has become possible to develop various human-computer interface systems. In this study, for the first-time a cross- correlation based data is reported for two-different types of bio-signals viz. EEG (Electroencephalography) and EMG (Electromyography). Whereas EEG refers to the neuro-electric impulses generated in the brain recorded in the form of electric potentials, EMG …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 9, Issue 2, 2019 · pp. 27–38 Read article
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A Profound Analysis on Complex Multiclass BCI Signals Classification by an EEG Device
Abstract: With a major shift in the technology paradigm today in the area of deep learning and artificial intelligence, one of its important applications today is found in brain-computer interface (BCI). Brain computer-interface, also known as neural control interface or mind-machine interface is a device that helps a user to interact with a computer using only his brain-activity, which is measured usually by Electroencephalography (EEG). The usage of such mechanism has …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 8, Issue 1, 2021 · pp. 22–44 Read article
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Advancing EEG Technology for Affordable and Effective Epilepsy Detection
Abstract: For a proper diagnosis and prompt treatment, epilepsy, a neurological condition marked by recurring seizures, needs to be continuously monitored. Manual interpretation is frequently used in traditional approaches for identifying epileptic seizures from electroencephalogram (EEG) signals, which can be laborious and error-prone. In this research, a novel method for automatically detecting epilepsy from EEG data using deep learning algorithms is presented. According to centers for disease control and prevention (CDC) …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 11–18 Read article
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Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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Analysis of impact of Meditation on Cognitive Workload using EEG Signals
Abstract: AbstractMental activities can be indicated by the Cognitive workload which are useful in applications like Biomedical, Human Machine Interaction and Task analysis. The mental effort applied on the Working memory at a certain given time is commonly known as Cognitive load. The EEG Signals of Cognitive Workload can be studied and classified. The features such as Entropy, Energy, Power, etc. can be extracted from the EEG signals and processed using …
Published in Current Trends in Signal Processing · Vol. 10, Issue 1, 2020 · pp. 29–39 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation · Vol. 14, Issue 1, 2023 · pp. 9–22 Read article
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Evaluation of OTA Amplifier Using EEG Signals
Abstract: AbstractOver the last few years, there has been a tremendous exploration in VLSI industries in response to scaling trends towards deep submicron technology. Demand for low power and efficient amplification are rising in day-to-day life. In the process of scaling the CMOS nanometer demand low supply, which is helped to design digital circuit realization at very low power consumption. But it is not valid for analog circuit realization. The related …
Published in Trends in Opto-electro & Optical Communication · Vol. 10, Issue 2, 2020 · pp. 5–14 Read article
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ELECTROENCEPHALOGY (EEG) SIGNALS AND ITS EXPERIMENT DESIGN – A STUDY
Abstract: Human brain is a very complex structure of the whole body which actually contains millions of the neurons which are playing a vital role in controlling and maintaining the behaviour of the human body when we talk about the internal or external sensory stimuli. These neurons actually carry the message from the brain to body and body to brain. It acts as the messenger between the body and the brain. …
Published in Recent Trends in Parallel Computing · Vol. 5, Issue 1, 2018 · pp. 15–19 Read article
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EEG Data Compression Using Communication Techniques for PMS
Abstract: Compression of EEG (Biological) is an important issue as concern to the telemedicine area. Many of researchers have carried out the work in this domain but still the area provides an ample space for research. This research aims to develop algorithm for the compression of EEG data. In this research the constructive communication modules are used to compress the EEG data. Thirty two channels are used to load the real …
Published in Current Trends in Signal Processing · Vol. 5, Issue 3, 2015 · pp. 1–6 Read article
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Entropy Analysis of EEG between Alcoholic and Control Subjects during Visual Stimulus Perception
Abstract: The purpose of this paper is to examine the variation in the EEG signal recorded from a group of individual, who were subjected under the effect of alcoholism while comparing them to non-alcoholic or controlled state. For the purpose, EEG from individuals was processed while subjects were perceiving visual picture stimulus from Snodgrass and Vanderwart picture set. A total of 10 trials for each subject have been considered in matched/non-matched …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 6, Issue 3, 2016 · pp. 5–14 Read article
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Entropy Analysis Based Differential Evolution Approach for Emotion Classification for EEG
Abstract: Electroencephalography (EEG) signal processing is having its significance in various applications related to the emotion recognition and classification. The behavior monitoring, behavior class identification, emotion class identification are the major aspects for classification of EEG signal. In this paper, a feature adaptive differential evolution (DE) approach is defined to perform emotion classification. In this work, we used discrete wavelet transform (DWT), for extracting the statistical features from the EEG signal …
Published in Current Trends in Signal Processing · Vol. 5, Issue 3, 2015 · pp. 15–22 Read article